User Modeling for Adaptive News Access

  • Daniel Billsus
  • Michael J. Pazzani


We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.

user modeling machine learning information retrieval intelligent agents recommender systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Allan, J., Carbonell, J., Doddington, G., Yamron, J. and Yang, Y.: 1998, Topic detection and tracking pilot study final report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 194-218, Lansdowne, VA.Google Scholar
  2. Balabanovic, M.: 1998, Learning to surf: multiagent systems for adaptive web page recommendation. Ph.D. Thesis, Stanford University.Google Scholar
  3. Bauer, M., Gmytrasiewicz, P. and Pohl, W.: 1999, Machine learning for user modeling, Seventh International Conference on User Modeling, Banff, Canada.Google Scholar
  4. Belkin, N.: 1997, User modeling in information retrieval. [online]. Available: (June 7, 2000).Google Scholar
  5. Billsus, D. and Pazzani, M.: 1999a, A personal news agent that talks, learns and explains, Proceedings of the Third International Conference on Autonomous Agents, Seattle, WA, pp. 268-275.Google Scholar
  6. Billsus, D. and Pazzani, M.: 1999b, A hybrid user model for news story classification, User Modeling: Proceedings of the Seventh International Conference (UM99), Banff, Canada, pp. 98-108.Google Scholar
  7. Chiu, B. and Webb, G.: 1998, Using decision trees for agent modeling: improving prediction performance. User Modeling and User-Adapted Interaction, 8, 131-152.Google Scholar
  8. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M.: 1999, Combining content-based and collaborative filters in an online newspaper. ACMSIGIR Workshop on Recommender Systems, Berkeley, CA.Google Scholar
  9. Cohen, W. and Hirsh, H.: 1998, Joins that generalize: text classi¢cation using WHIRL, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY, pp. 169-173.Google Scholar
  10. Dietterich, T.: 1998, Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895-1924.Google Scholar
  11. Duda, R. and Hart, P.: 1973, Pattern Classification and Scene Analysis, New York, NY: Wiley.Google Scholar
  12. Jameson, A., Paris, C. and Tasso, C. (eds.): 1997, User Modeling: Proceedings of the Sixth International Conference (UM97), New York: Springer.Google Scholar
  13. Joachims, T., McCallum, A., Sahami, M. and Ungar, L. (eds.): 1999, IJCAI Workshop IRF2: Machine Learning for Information Filtering, Stockholm, Sweden.Google Scholar
  14. Kay, J. (ed.).: 1999, User Modeling: Proceedings of the Seventh International Conference (UM99), Banff, Canada.Google Scholar
  15. Klinkenberg, R. and Renz, I.: 1998, Adaptive information filtering: learning in the presence of concept drift, AAAI/ICML-8Workshop on Learning for Text Categorization, Technical Report WS-98-05, Madison, WI.Google Scholar
  16. Lang, K.: 1995, NewsWeeder: learning to filter news, Proceedings of the Twelfth International Machine Learning Conference (ICML '95), Lake Tahoe, CA, pp. 331-339.Google Scholar
  17. Lewis, D. and Gale, W.A.: 1994, A sequential algorithm for training text classifiers, Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 3-12.Google Scholar
  18. Lieberman, H.: 1995, Letizia: An agent that assists web browsing, Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 924-929.Google Scholar
  19. McCallum, A. and Nigam, K.: 1998, A comparison of event models for naive bayes text classification, AAAI/ICML-98Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press, pp. 41-48.Google Scholar
  20. Mooney, R., Bennet, P. and Roy, L.: 1998, Book recommending using text categorization with extracted information. AAAI/ICML-98Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press, pp. 49-54.Google Scholar
  21. Papatheodorou, C. (ed.).: 1999, Machine learning and applications workshop. Machine Learning in User Modeling, Chania, Greece.Google Scholar
  22. Pazzani, M. and Billsus, D.: 1997, Learning and revising user profiles: the identification of interesting web sites. Machine Learning, 27, 313-331.Google Scholar
  23. Quinlan, J.: 1986, Induction of decision trees. Machine Learning, 1, 81-106.Google Scholar
  24. Rocchio, J. (1971). Relevance feedback in information retrieval, In: G. Salton (ed.). The SMART System: Experiments in Automatic Document Processing, NJ: Prentice Hall, pp. 313-323.Google Scholar
  25. Rudstrom, A., Bauer, M., Iba, W. and Pohl, W. (eds.).: 1999, IJCAI Workshop ML4: Learning About Users, Stockholm, Sweden.Google Scholar
  26. Sakagami, H. and Kamba, T.: 1997, Learning personal preferences on online newspaper articles from user behaviors. Proceedings of the Sixth International World Wide Web Conference (WWW6), Santa Clara, CA, pp. 291-300.Google Scholar
  27. Salton, G.: 1989, Automatic Text Processing, Addison-Wesley.Google Scholar
  28. Shardanand, U. and Maes, P.: 1995, Social information filtering: algorithms for automating `word of mouth', Proceedings of the Conference on Human Factors in Computing Systems (CHI95), Denver, CO, pp. 210-217.Google Scholar
  29. Veltman, G.: 1998, A multi-agent system for generating a personalized newspaper digest. AAAI/ICML-98 Workshop on Learning for Text Categorization, Technical Report WS-98-05, AAAI Press, pp. 99-102.Google Scholar
  30. Webb, G., Chiu, C. and Kuzmycz, M.: 1997, Comparative evaluation of alternative induction engines for feature based modeling. International Journal of Arti¢cial Intelligence in Education, 8, 97-115.Google Scholar
  31. Webb, G. and Kuzmycz, M.: 1996, Feature based modeling: a methodology for producing coherent, consistent, dynamically changing models of agents' competencies. User Modeling and User Assisted Interaction, 5(2), 117-150.Google Scholar
  32. Widmer, G. and Kubat, M.: 1996, Learning in the presence of concept drift and hidden contexts. Machine Learning, 23, 69-101.Google Scholar
  33. Yang, Y.: 1999, An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1), 67-88.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Daniel Billsus
    • 1
  • Michael J. Pazzani
    • 1
  1. 1.Dept. of Information and Computer ScienceUniversity of CaliforniaIrvineUSA

Personalised recommendations